import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import plotly
import plotly.express as px
import missingno as msno
import ipywidgets as widgets
import geopandas as gpd
plt.style.use('seaborn-v0_8')
print("Plotly version:", plotly.__version__)
print('matplotlib: {}'.format(matplotlib.__version__))

Plotly version: 5.9.0
matplotlib: 3.7.1

df_trips = pd.read_csv('citibike-trips.csv')
df_stations = pd.read_csv('citibike-stations.csv')

Comments about the coloumns meaning

Trip Duration (in seconds)</br> Start Time, in NYC local time.</br> Stop Time, in NYC local time.</br>

df_trips.head(3)

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	customer_plan
0	2319	2016-03-09 13:08:21	2016-03-09 13:47:01	520	W 52 St & 5 Ave	40.759923	-73.976485	363	West Thames St	40.708347	-74.017134	23062	Subscriber	1972.0	male	NaN
1	313	2015-07-09 15:42:30	2015-07-09 15:47:44	520	W 52 St & 5 Ave	40.759923	-73.976485	493	W 45 St & 6 Ave	40.756800	-73.982912	16909	Subscriber	1968.0	female	NaN
2	798	2017-04-20 18:43:59	2017-04-20 18:57:17	520	W 52 St & 5 Ave	40.759923	-73.976485	3258	W 27 St & 10 Ave	40.750182	-74.002184	15555	Subscriber	1991.0	male	NaN

df_trips.dtypes

tripduration                 int64
starttime                   object
stoptime                    object
start_station_id             int64
start_station_name          object
start_station_latitude     float64
start_station_longitude    float64
end_station_id               int64
end_station_name            object
end_station_latitude       float64
end_station_longitude      float64
bikeid                       int64
usertype                    object
birth_year                 float64
gender                      object
customer_plan              float64
dtype: object

df_trips['starttime'] = pd.to_datetime(df_trips['starttime'],format='%Y-%m-%d %H:%M:%S')
df_trips['Year'] = df_trips['starttime'].dt.strftime('%Y')
df_trips['Year_Month'] = df_trips['starttime'].dt.strftime('%Y-%b')
df_trips['Month'] = df_trips['starttime'].dt.strftime("%B")
df_trips['Day'] = df_trips['starttime'].dt.strftime('%d')
df_trips['Day_Name'] = df_trips['starttime'].dt.strftime("%A")

df_trips['starttime'].dtype

dtype('<M8[ns]')

# missingno library is a useful tool to vizualize the missing values
msno.bar(df_trips)

<Axes: >

df_trips['birth_year'].fillna(value = 0, inplace=True, downcast='int64')

df_trips['birth_year'].isnull().sum()

0

df_trips.drop('customer_plan',axis=1,inplace=True)

df_trips.describe().round(1)

	tripduration	start_station_id	start_station_latitude	start_station_longitude	end_station_id	end_station_latitude	end_station_longitude	bikeid	birth_year
count	473556.0	473556.0	473556.0	473556.0	473556.0	473556.0	473556.0	473556.0	473556.0
mean	944.2	879.4	40.7	-74.0	873.1	40.7	-74.0	20644.6	1743.6
std	6760.5	1058.2	0.0	0.0	1053.0	0.1	0.1	4464.8	638.5
min	60.0	72.0	40.6	-74.0	72.0	0.0	-74.0	14529.0	0.0
25%	388.0	326.0	40.7	-74.0	326.0	40.7	-74.0	17103.0	1964.0
50%	633.0	446.0	40.7	-74.0	445.0	40.7	-74.0	19615.5	1978.0
75%	1069.0	521.0	40.8	-74.0	520.0	40.8	-74.0	23462.0	1986.0
max	1969746.0	3649.0	40.8	-73.9	3654.0	40.8	0.0	33473.0	2001.0

def print_outliers_IQR(df):
    q1=df.quantile(0)
    q3=df.quantile(0.999)
    IQR=q3-q1
    return print(f"IQR:{IQR}\nq3:{q3}\nq1:{q1}")

print_outliers_IQR(df_trips['tripduration'])

IQR:20222.34500000137
q3:20282.34500000137
q1:60.0

def find_outliers_IQR(df):
    q1=df.quantile(0.25)
    q3=df.quantile(0.999)
    IQR=q3-q1
    outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]
    return outliers

df_outliers = pd.DataFrame(find_outliers_IQR(df_trips['tripduration']))
df_outliers

	tripduration
183	183705
6278	60392
7904	187312
10147	257031
19019	64467
...	...
463432	224243
464148	63541
466584	445073
470997	170676
471099	61433

206 rows × 1 columns

df_outliers['tripduration'].min()

50608

df_trips = df_trips[(df_trips['tripduration']<df_outliers['tripduration'].min())]

df_trips_noZero_BthYears = df_trips[(df_trips['birth_year']>0)]

#checking row count without the null(0) birthdays
df_trips_noZero_BthYears.tripduration.count()

417468

df_trips.head(1)

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	Year	Year_Month	Month	Day	Day_Name
0	2319	2016-03-09 13:08:21	2016-03-09 13:47:01	520	W 52 St & 5 Ave	40.759923	-73.976485	363	West Thames St	40.708347	-74.017134	23062	Subscriber	1972	male	2016	2016-Mar	March	09	Wednesday

df_days = df_trips.groupby('Day_Name').size()
df_days = df_days/df_days.sum()*100
df_days = df_days.reset_index(name='Percentage')
df_days

	Day_Name	Percentage
0	Friday	14.842294
1	Monday	14.344143
2	Saturday	12.316468
3	Sunday	12.013098
4	Thursday	15.374670
5	Tuesday	15.336854
6	Wednesday	15.772473

colorscales = px.colors.named_colorscales()

fig = px.bar(df_days,
            category_orders={"Day_Name": ["Monday", "Tuesday", "Wednesday", "Thursday",'Friday','Saturday','Sunday']},
            text=[f'{x:.1f}%' for x in df_days.Percentage],  # Convert to a list and format each element            
            orientation="v",
            x = df_days.Day_Name,
            y = df_days.Percentage,
            #color = df_days.Percentage,
            #color_discrete_sequence = ['chartreuse'],
            #color_continuous_scale='picnic',
            labels={
                     "Day_Name": "Day Of Week",
                     "Percentage": "Percentagem",
                 }
            )

fig.update_layout(yaxis_range=[0,20],
                 showlegend=False,
                 title='% of Riders By Day of Week',
                 )
#colorscales = px.colors.named_colorscales() #List to check color scales/palettes
fig.show()

df_years = df_trips.groupby('Year').size()
df_years = df_years.reset_index(name='Count')
df_years

	Year	Count
0	2013	50697
1	2014	80520
2	2015	99174
3	2016	102310
4	2017	140649

fig = px.bar(df_years,
            category_orders={"Year": ["2013", "2014", "2015", "2016",'2017']},
            text=[f'{x}' for x in df_years.Count],  # Convert to a list and format each element            
            orientation="v",
            x = df_years.Year,
            y = df_years.Count,
            #color = df_days.Percentage,
            #color_discrete_sequence = ['chartreuse'],
            #color_continuous_scale='picnic',
            labels={
                     "Year": "Year",
                     "Count": "Number of Riders",
                 }
            )

fig.update_layout(
                 showlegend=False,
                 title='Number of Riders By Year',
                 )
#colorscales = px.colors.named_colorscales() #List to check color scales/palettes
fig.show()

df_YM = df_trips.groupby(['Year_Month','Day']).size()
df_YM = df_YM.reset_index(name='Count')
df_YM

	Year_Month	Day	Count
0	2013-Aug	01	205
1	2013-Aug	02	375
2	2013-Aug	03	293
3	2013-Aug	04	373
4	2013-Aug	05	361
...	...	...	...
1454	2017-Sep	26	677
1455	2017-Sep	27	667
1456	2017-Sep	28	715
1457	2017-Sep	29	704
1458	2017-Sep	30	480

1459 rows × 3 columns

# Define the function to update the plot
def update_plot(Year_Month):
    
    filtered_df = df_YM[(df_YM['Year_Month']== Year_Month)]
    
    fig = px.bar(filtered_df,
                #category_orders={"Year": ["2013", "2014", "2015", "2016",'2017']},
                #text=[f'{x}' for x in df_years.Count],  # Convert to a list and format each element            
                orientation="v",
                x = filtered_df.Day,
                y = filtered_df.Count,
                #color = df_days.Percentage,
                #color_discrete_sequence = ['chartreuse'],
                #color_continuous_scale='picnic',
                labels={
                         "Day": "Day of Month",
                         "Count": "Number of Riders per day",
                     }
                )
    
    fig.update_layout(
                     showlegend=False,
                     title='Number of Riders per Day',
                     )
    #colorscales = px.colors.named_colorscales() #List to check color scales/palettes
    fig.show()
    
Column_Values_Year = df_YM.Year_Month.unique()

dropdown_Year=widgets.Dropdown(
    options=Column_Values_Year,
    value=Column_Values_Year[0],
    description='Year:',
    disabled=False,
)

# Link slider to update function
widgets.interact(update_plot, Year_Month = dropdown_Year)

interactive(children=(Dropdown(description='Year:', options=('2013-Aug', '2013-Dec', '2013-Jul', '2013-Nov', '…

<function __main__.update_plot(Year_Month)>

df_gender = df_trips[df_trips['gender']!='unknown']
df_gender = df_gender.groupby(['gender']).size()
df_gender = df_gender/df_gender.sum()*100
df_gender = df_gender.reset_index(name='Percentage')
df_gender

	gender	Percentage
0	female	24.203666
1	male	75.796334

fig = px.pie(df_gender, 
             values=df_gender.Percentage, 
             labels={
                         "gender": "Gender",
                         "Percentage": "Percentage",
                     },
             names=df_gender.gender,
             hole=.3)

fig.update_layout(showlegend=True,
                  title='% of man and wom',
                  #textposition='inside', 
                  #textinfo='percent+label'
                 )
#colorscales = px.colors.named_colorscales() #List to check color scales/palettes
fig.show() 

df_trips.head(1)

	tripduration	starttime	stoptime	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bikeid	usertype	birth_year	gender	Year	Year_Month	Month	Day	Day_Name
0	2319	2016-03-09 13:08:21	2016-03-09 13:47:01	520	W 52 St & 5 Ave	40.759923	-73.976485	363	West Thames St	40.708347	-74.017134	23062	Subscriber	1972	male	2016	2016-Mar	March	09	Wednesday

start_station_name start_station_latitude start_station_longitude count (.size)

df_Start_Station = df_trips.groupby(['start_station_id','start_station_name','start_station_latitude','start_station_longitude']).size()
df_Start_Station = df_Start_Station.reset_index(name='Count')
df_Start_Station['start_station_name'].nunique()
df_Start_Station.loc[3] == df_Start_Station.loc[4]
df_Start_Station

	start_station_id	start_station_name	start_station_latitude	start_station_longitude	Count
0	72	W 52 St & 11 Ave	40.767272	-73.993929	1226
1	79	Franklin St & W Broadway	40.719116	-74.006667	996
2	82	St James Pl & Pearl St	40.711174	-74.000165	421
3	83	Atlantic Ave & Fort Greene Pl	40.683826	-73.976323	537
4	116	W 17 St & 8 Ave	40.741776	-74.001497	1957
...	...	...	...	...	...
892	3643	E 41 St & 5 Ave	40.752722	-73.981236	73
893	3644	Van Dyke St & Van Brunt St	40.675816	-74.014882	11
894	3647	48 Ave & 30 Pl	40.741283	-73.937259	5
895	3648	Flushing Ave & Vanderbilt Ave	40.697950	-73.970776	3
896	3649	W 129 St & Convent Ave	40.814394	-73.953247	3

897 rows × 5 columns

#px.set_mapbox_access_token(open(".mapbox_token").read())

fig = px.scatter_mapbox(df_Start_Station,
                        lat="start_station_latitude", 
                        lon="start_station_longitude",     
                        color="Count",
                        size="Count",
                        color_continuous_scale=px.colors.cyclical.IceFire,
                        size_max=15, 
                        zoom=10,
                        center={'lat':40.7307, 'lon':-74.0060},
                        hover_name='start_station_name',
                        labels={
                         "start_station_name": "Station Name",
                         "start_station_latitude": "Latitude",
                         "start_station_longitude": "Longitude",
                         "Count": "Trip Count",
                     },)

fig.update_layout(mapbox_style="carto-positron")
fig.show()

df_Start_End_Station = df_trips.groupby(['start_station_id','start_station_name','start_station_latitude','start_station_longitude','end_station_latitude','end_station_longitude']).size()
df_Start_End_Station = df_Start_End_Station.reset_index(name='Trip_Count')
df_Start_End_Station = df_Start_End_Station[(df_Start_End_Station['start_station_id']==72)]
df_Start_End_Station

	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_latitude	end_station_longitude	Trip_Count
0	72	W 52 St & 11 Ave	40.767272	-73.993929	40.683125	-73.978951	1
1	72	W 52 St & 11 Ave	40.767272	-73.993929	40.685160	-73.977114	1
2	72	W 52 St & 11 Ave	40.767272	-73.993929	40.690238	-73.992031	1
3	72	W 52 St & 11 Ave	40.767272	-73.993929	40.692418	-73.989495	1
4	72	W 52 St & 11 Ave	40.767272	-73.993929	40.694247	-73.992159	1
...	...	...	...	...	...	...	...
264	72	W 52 St & 11 Ave	40.767272	-73.993929	40.798994	-73.966217	1
265	72	W 52 St & 11 Ave	40.767272	-73.993929	40.799757	-73.962113	1
266	72	W 52 St & 11 Ave	40.767272	-73.993929	40.801343	-73.971146	4
267	72	W 52 St & 11 Ave	40.767272	-73.993929	40.804213	-73.966991	2
268	72	W 52 St & 11 Ave	40.767272	-73.993929	40.812056	-73.961255	1

269 rows × 7 columns

df_Start_End_Station = df_Start_End_Station.explode(['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude', 'start_station_name'])

df_Start_End_Station

	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_latitude	end_station_longitude	Trip_Count
0	72	W 52 St & 11 Ave	40.767272	-73.993929	40.683125	-73.978951	1
1	72	W 52 St & 11 Ave	40.767272	-73.993929	40.685160	-73.977114	1
2	72	W 52 St & 11 Ave	40.767272	-73.993929	40.690238	-73.992031	1
3	72	W 52 St & 11 Ave	40.767272	-73.993929	40.692418	-73.989495	1
4	72	W 52 St & 11 Ave	40.767272	-73.993929	40.694247	-73.992159	1
...	...	...	...	...	...	...	...
264	72	W 52 St & 11 Ave	40.767272	-73.993929	40.798994	-73.966217	1
265	72	W 52 St & 11 Ave	40.767272	-73.993929	40.799757	-73.962113	1
266	72	W 52 St & 11 Ave	40.767272	-73.993929	40.801343	-73.971146	4
267	72	W 52 St & 11 Ave	40.767272	-73.993929	40.804213	-73.966991	2
268	72	W 52 St & 11 Ave	40.767272	-73.993929	40.812056	-73.961255	1

269 rows × 7 columns

# Create a line mapbox plot
fig = px.line_mapbox(df_Start_End_Station,
                     lat=['start_station_latitude', 'end_station_latitude'],
                     lon=['start_station_longitude', 'end_station_longitude'],
                     #hover_data=['start_station_latitude','end_station_longitude'],
                     #color='Trip_Count', 
                     #line_width='Trip_Count',
                     zoom=11.3, center={'lat': 40.767272, 'lon': -73.993929},
                     mapbox_style='carto-positron')

fig.update_traces(#opacity=0.5,
                  line=dict(color='#195185'))

fig.show()

#fig.update_layout(mapbox_style="carto-positron")
#fig.show()